On the Advantage of Adaptivity for Sampling with Cell Probes
This work resolves the optimal separation between adaptive and non-adaptive sampling in the cell-probe model, a fundamental question in data structures and sampling theory.
The authors construct a distribution over {0,1}^N that can be sampled with 2 adaptive cell probes but requires Ω̃(N) non-adaptive probes, achieving an optimal separation between adaptive and non-adaptive sampling in the cell-probe model.
We construct an explicit distribution $\mathbf{D}$ over $\{0,1\}^N$ that exhibits an essentially optimal separation between adaptive and non-adaptive cell-probe sampling. The distribution can be sampled exactly when each output bit is allowed two adaptive probes to an arbitrarily long sequence of independent uniform symbols from $[N]$. In contrast, any non-adaptive sampler requires $\widetildeΩ(N)$ non-adaptive cell probes to generate a distribution with total variation distance less than $1-o(1)$ from $\mathbf{D}$. This provides a $2$-vs-$\widetildeΩ(N)$ separation for sampling with adaptive versus non-adaptive cell probes, improving upon the $2$-vs-$\widetildeΩ(\log N)$ separation of Yu and Zhan (ITCS '24) and the $(\log N)^{O(1)}$-vs-$N^{Ω(1)}$ separation of Alekseev, Göös, Myasnikov, Riazanov, and Sokolov (STOC '26).